Example of a contour plot for a binary logistic regression model
main topic     interpreting results     session command     see also 

One product that a company makes are molded resin parts. The company knows that contamination in pipes and abrasions during transfer through hoses can lead to discolored streaks in the final product. A larger screw passes the pellets through the hoses faster. With a new type of resin pellet, the company decides to collect data on these discoloration defects to learn about the best way to transfer pellets.

1    Open the worksheet ResinDefects.MTW.

2    Choose Stat > Regression > Poisson Regression > Fit Poisson Model.

3    In Response, enter 'Discoloration Defects'.

4    In Continuous predictors, enter 'Hours Since Cleanse' and Temperature.

5    In Categorical predictors, enter 'Size of Screw'.

6    Click Model.

7    In Predictors, select 'Temperature' and 'Size of Screw'.

8    For Interactions through order, choose 2.

9    Next to Interactions through order, click Add.

10  Click OK in both dialog boxes.

11  Choose Stat > Regression > Poisson Regression > Contour Plot.

12  In Variables select Generate plots for all pairs of continuous variables.

13 In Generate plots for all pairs of continuous variables select On separate graphs.

14  Click OK.

15  To recall the last dialog box, press [Ctrl]+[E].

16  Press Settings.

17 In Hold categorical variables at choose small.

18  Click OK in both dialog boxes.

Graph window output

Interpreting the results

The size of the screw is a categorical variable. Therefore, it makes sense to set the size of the screw at its different levels and compare the plots. The response is the number of discoloration defects.

On the two plots, the contours move in roughly the same direction. Higher values of time and lower values of temperature increase the number of defects.

The legend clarifies the effect of the size of the screw. The range of defects when the screw is large is from less than 50 to over 80. The range of defects when the screw is small is from less than 52 to over 62. The change in the number of defects is less when the screw is small.

The plots use a model equation. Ensure that your model is adequate before you interpret the plots.